elastic/detection-rules

Features

• Threat Detection Rule Repositories - Provides a centralized collection of predefined security rules for detecting threats and anomalies within Elasticsearch indices.
• API Query Languages - Implements structured query capabilities to retrieve security events and telemetry from Elasticsearch indices.
• Syntax Validation - Provides a validation utility to ensure rule files are syntactically correct and schema-compliant.
• Detection Rule Deployment - Ships tools to automate the import and export of detection rules to the security engine.
• Cloud Security Monitoring - Monitors cloud and Kubernetes workload sessions and audits security posture to detect threats.
• Prebuilt Detection Rule Packs - Offers a library of prebuilt detection rule packs that can be imported and customized for specific environments.
• Data Stream Evaluators - Implements a scanning mechanism that evaluates incoming data streams against predefined criteria to generate security alerts.
• Detection-as-Code Frameworks - Provides a complete framework for managing security detections through version control and automated validation.
• Runtime Threat Detection - Identifies malicious activity by correlating system events and monitoring runtime behavioral patterns.
• SIEM Content Repositories - Ships a library of security monitoring rules and detection content designed for SIEM platforms.
• Rule Lifecycle Management - Provides a programmatic API to manage the full lifecycle of security detection configurations.
• Security Rule Development - Implements a code-based workflow to create, parse, and validate security rules for consistency.
• YAML Security Rule Definition - Uses YAML files to define detection logic and metadata, enabling security rules to be managed as versioned code.
• Metadata Schema Validations - Validates that rule metadata and structural definitions adhere to a predefined schema during the build process.
• Detection Rule Testing - Provides a Python-based testing framework to validate that security rules trigger correctly and produce expected results.
• Unit Testing - Includes a Python-based testing framework to validate detection accuracy and rule logic using sample data.
• Threat Intelligence - Integrates external threat intelligence feeds and indicators to improve detection accuracy.
• Rule Template Generators - A command line interface in the product that guides the creation of new rule files with required metadata.
• Security Event Correlation - Analyzes patterns across multiple data sources to uncover complex security threats.
• Detection Rule Search - Includes a command-line utility to locate specific security rules by querying their metadata.
• Cloud Security Posture Management - Assesses security posture and workload sessions across cloud and Kubernetes environments.
• Threat Intelligence Aggregation - Ingests and manages threat intelligence feeds to improve the accuracy of security detections.
• Entity Risk Analysis - Provides a graph-based system to track risk scores and entity relationships to identify compromised assets.
• Rule Scaffolding - Provides CLI-based scaffolding to generate new rule files with consistent metadata structures.
• Alert Suppression Systems - Includes capabilities to reduce alert fatigue through muting, deduplication, and scheduled maintenance windows.
• Alerting and Incident Management - Manages security alerts through noise suppression and formal incident coordination workflows.
• Anomaly Detection - Implements machine learning capabilities to analyze time series and logs for identifying rare events.
• Incident Management - Coordinates security incident response through a formal case management workflow and user assignments.
• Security Event Monitoring - Provides tools to explore timeline events and analyze host, network, and user activity for attack patterns.
• Detection Content Libraries - Contains native detection rules designed for the Elastic SIEM platform.
• Detection Engineering - Official detection rules for the Elastic Stack.

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